Vehicle Detection Research Articles

Research on the detection and recognition system of target vehicles based on fusion algorithm

<p>In modern society, the monitoring of vehicles is paramount for upholding traffic safety and order. This paper delves into a Graphical User Interface (GUI)-driven system for vehicle target detection and integration. This system seamlessly integrates three pivotal functionalities: vehicle type recognition, license plate recognition, and driver face recognition. It automates the vehicle inspection assessment process through an intuitive user interface. Specifically, for license plate recognition, the system leverages traditional computer vision techniques, including color and grayscale processing, radon transform, morphological operations, edge detection, character segmentation, and character recognition. The driver face recognition module incorporates methods such as image gray scaling, Gaussian filtering for denoising, face detection, and feature extraction. Meanwhile, vehicle type recognition is achieved by extracting Histogram of Oriented Gradients (HOG) features and utilizing a Support Vector Machine (SVM) classifier. Experimental findings highlight the system’s remarkable recognition accuracy, offering a highly efficient and dependable solution for the automated inspections of vehicles.</p>

Vehicle Localization Method in Complex SAR Images Based on Feature Reconstruction and Aggregation.

Due to the small size of vehicle targets, complex background environments, and the discrete scattering characteristics of high-resolution synthetic aperture radar (SAR) images, existing deep learning networks face challenges in extracting high-quality vehicle features from SAR images, which impacts vehicle localization accuracy. To address this issue, this paper proposes a vehicle localization method for SAR images based on feature reconstruction and aggregation with rotating boxes. Specifically, our method first employs a backbone network that integrates the space-channel reconfiguration module (SCRM), which contains spatial and channel attention mechanisms specifically designed for SAR images to extract features. The network then connects a progressive cross-fusion mechanism (PCFM) that effectively combines multi-view features from different feature layers, enhancing the information content of feature maps and improving feature representation quality. Finally, these features containing a large receptive field region and enhanced rich contextual information are input into a rotating box vehicle detection head, which effectively reduces false alarms and missed detections. Experiments on a complex scene SAR image vehicle dataset demonstrate that the proposed method significantly improves vehicle localization accuracy. Our method achieves state-of-the-art performance, which demonstrates the superiority and effectiveness of the proposed method.

Combining deep learning methods and rule-based systems for automatic parking space detection

This paper presents an Automatic Parking Space Detection (APSD) algorithm designed to reduce traffic in cities while offering an information system of available parking zones. The main aim of such a system lies in its ability to identify parking spaces in a distributed manner, achieved by installing multiple APSD systems across a fleet of vehicles. This fleet, during its regular operations, communicates the availability of parking spaces to a centralized information system. Our methodology employs a rule-based system that seamlessly integrates a variety of neural networks for different specific tasks. These tasks include depth estimation, road segmentation, and vehicle detection. This approach would fall into a modular category instead of an end-to-end solution, using the Málaga Urban Dataset in the experiments. We present a preliminary experiment for parameter settings and an ablation study to quantify each subsystem contribution to the results. The proposed system achieves a parking space detection F1 score of 0.726.

A Novel Fuzzy Image-Based UAV Landing Using RGBD Data and Visual SLAM

In this work, an innovative perception-guided approach is proposed for landing zone detection and realization of Unmanned Aerial Vehicles (UAVs) operating in unstructured environments ridden with obstacles. To accommodate secure landing, two well-established tools, namely fuzzy systems and visual Simultaneous Localization and Mapping (vSLAM), are implemented into the landing pipeline. Firstly, colored images and point clouds acquired by a visual sensory device are processed to serve as characterizing maps that acquire information about flatness, steepness, inclination, and depth variation. By leveraging these images, a novel fuzzy map infers the areas for risk-free landing on which the UAV can safely land. Subsequently, the vSLAM system is employed to estimate the platform’s pose and an additional set of point clouds. The vSLAM point clouds presented in the corresponding keyframe are projected back onto the image plane on which a threshold fuzzy landing score map is applied. In other words, this binary image serves as a mask for the re-projected vSLAM world points to identify the best subset for landing. Once these image points are identified, their corresponding world points are located, and among them, the center of the cluster with the largest area is chosen as the point to land. Depending on the UAV’s size, four synthesis points are added to the vSLAM point cloud to execute the image-based visual servoing landing using image moment features. The effectiveness of the landing package is assessed through the ROS Gazebo simulation environment, where comparisons are made with a state-of-the-art landing site detection method.

Multi-scenario Vehicle Detection Based on the YOLOv5 Algorithm

This paper addresses the issues of increasing traffic volume leading to frequent traffic congestion and accidents by studying the effectiveness of occluded vehicle detection based on the YOLOv5 algorithm. The aim is to explore the performance of this algorithm in various occlusion scenarios. Firstly, we introduce the occluded vehicle images used for testing and describe different types of occlusions in detail. Then, the basic principles and construction process of the YOLOv5 algorithm are explained. Subsequently, we propose a vehicle detection and recognition model based on YOLOv5 and validate the model's effectiveness through convergence analysis of the training loss curve and accuracy analysis of the test set. Finally, experimental results demonstrate that the YOLOv5 algorithm can efficiently and accurately detect vehicles in complex scenarios and partially occluded conditions, showcasing its application potential in intelligent transportation systems.

LFIR-YOLO: Lightweight Model for Infrared Vehicle and Pedestrian Detection

The complexity of urban road scenes at night and the inadequacy of visible light imaging in such conditions pose significant challenges. To address the issues of insufficient color information, texture detail, and low spatial resolution in infrared imagery, we propose an enhanced infrared detection model called LFIR-YOLO, which is built upon the YOLOv8 architecture. The primary goal is to improve the accuracy of infrared target detection in nighttime traffic scenarios while meeting practical deployment requirements. First, to address challenges such as limited contrast and occlusion noise in infrared images, the C2f module in the high-level backbone network is augmented with a Dilation-wise Residual (DWR) module, incorporating multi-scale infrared contextual information to enhance feature extraction capabilities. Secondly, at the neck of the network, a Content-guided Attention (CGA) mechanism is applied to fuse features and re-modulate both initial and advanced features, catering to the low signal-to-noise ratio and sparse detail features characteristic of infrared images. Third, a shared convolution strategy is employed in the detection head, replacing the decoupled head strategy and utilizing shared Detail Enhancement Convolution (DEConv) and Group Norm (GN) operations to achieve lightweight yet precise improvements. Finally, loss functions, PIoU v2 and Adaptive Threshold Focal Loss (ATFL), are integrated into the model to better decouple infrared targets from the background and to enhance convergence speed. The experimental results on the FLIR and multispectral datasets show that the proposed LFIR-YOLO model achieves an improvement in detection accuracy of 4.3% and 2.6%, respectively, compared to the YOLOv8 model. Furthermore, the model demonstrates a reduction in parameters and computational complexity by 15.5% and 34%, respectively, enhancing its suitability for real-time deployment on resource-constrained edge devices.

Enhanced Vehicle Logo Detection Method Based on Self-Attention Mechanism for Electric Vehicle Application

Vehicle logo detection plays a crucial role in various computer vision applications, such as vehicle classification and detection. In this research, we propose an improved vehicle logo detection method leveraging the self-attention mechanism. Our feature-sampling structure integrates multiple attention mechanisms and bidirectional feature aggregation to enhance the discriminative power of the detection model. Specifically, we introduce the multi-head attention for multi-scale feature fusion module to capture multi-scale contextual information effectively. Moreover, we incorporate the bidirectional aggregation mechanism to facilitate information exchange between different layers of the detection network. Experimental results on a benchmark dataset (VLD-45 dataset) demonstrate that our proposed method outperforms baseline models in terms of both detection accuracy and efficiency. Our experimental evaluation using the VLD-45 dataset achieves a state-of-the-art result of 90.3% mAP. Our method has also improved AP by 10% for difficult samples, such as HAVAL and LAND ROVER. Our method provides a new detection framework for small-size objects, with potential applications in various fields.

Vehicle and Pedestrian Detection Based on Improved YOLOv7-Tiny

To improve the detection accuracy of vehicles and pedestrians in traffic scenes using object detection algorithms, this paper presents modifications, compression, and deployment of the single-stage typical algorithm YOLOv7-tiny. In the model improvement section: firstly, to address the problem of small object missed detection, shallower feature layer information is incorporated into the original feature fusion branch, forming a four-scale detection head; secondly, a Multi-Stage Feature Fusion (MSFF) module is proposed to fully integrate shallow, middle, and deep feature information to extract more comprehensive small object information. In the model compression section: the Layer-Adaptive Magnitude-based Pruning (LAMP) algorithm and the Torch-Pruning library are combined, setting different pruning rates for the improved model. In the model deployment section: the V7-tiny-P2-MSFF model, pruned by 45% using LAMP, is deployed on the embedded platform NVIDIA Jetson AGX Xavier. Experimental results show that the improved and pruned model achieves a 12.3% increase in mAP@0.5 compared to the original model, with parameter volume, computation volume, and model size reduced by 76.74%, 7.57%, and 70.94%, respectively. Moreover, the inference speed of a single image for the pruned and quantized model deployed on Xavier is 9.5 ms.

Enhancing real‐time traffic volume prediction: A two‐step approach of object detection and time series modelling

AbstractA two‐step framework that integrates real‐time data collection with time series forecasting models for predicting traffic volume is proposed. In the first step, the framework utilizes live highway surveillance video data and YOLO‐v7 object detector to construct accurate traffic volume data. In the second step, an ARIMA–LSTM time series model is applied to forecast future traffic volumes. Experimental results show that YOLO‐v7 achieved a vehicle detection accuracy of over 93.30%, ensuring high precision in traffic volume data construction. The ARIMA–LSTM model demonstrated superior performance in traffic volume prediction, with a mean squared error of 87.97, root mean squared error of 10,388.57, and mean absolute error of 101.39. YOLO‐v7's detection speed of 7.8 ms per frame further validates the feasibility of real‐time data construction. The findings indicate that the combination of YOLO‐v7 for vehicle detection and ARIMA–LSTM for traffic prediction is highly effective, offering a significant reduction in training time compared to more complex deep learning models while maintaining high prediction accuracy. This research presents a unified solution for traffic data collection and prediction, enhancing transportation infrastructure planning and optimizing traffic flow. Future work will focus on extending the prediction intervals and further refining the models to improve performance.

Vehicle object detection and ranging in vehicle images based on deep learning

In order to improve the accuracy of vehicle target detection and the stability of ranging in driving environments, a vehicle target detection and ranging method based on deep learning is proposed. The YOLOX-S algorithm is used as the vehicle target detection framework for improvement: the CBAM attention module is introduced on the basis of the original algorithm to enhance the network feature expression ability, and the confidence loss function is replaced by Focal Loss to reduce the training weight of simple samples and improve the attention of positive samples. The vehicle ranging model is established according to the imaging principle and geometric relationship of the vehicle camera, and the ranging feature point coordinates and camera internal parameters are input to obtain the ranging results. The self-made Tlab dataset and BDD 100K dataset are used to train and evaluate the improved YOLOX-S algorithm, and a static ranging experimental scene is built to verify the vehicle ranging model. The experimental results show that the improved YOLOX-S algorithm has a detection speed of 70.14 frames per second on the experimental data set. Compared with the original algorithm, the precision, recall, F1 value, and mAP are improved respectively 0.86%、1.32%、1.09%、1.54% ; within the measurement range of 50 m in the longitudinal direction and 11.25 m in the lateral direction, the average ranging error is kept within 3.20% . It can be seen that the proposed method has good vehicle ranging accuracy and stability while meeting the real-time requirements of vehicle detection.

Video Content Analysis for Detection of Road Traffic Congestion Pattern: An Optimized YOLOv8 Based Approach

This paper presents a new approach to Video Content Analysis (VCA) with a specific focus on detecting and analyzing road traffic congestion patterns. The implemented methodology deploys the YOLOv8 (You Only Look Once) object detection framework, which has been optimized to accurately and efficiently identify road traffic objects within video streams. The primary goal is to improve traditional VCA systems by capturing and understanding complex road congestion scenarios. This research develops a customized YOLO model optimized specifically for road traffic analysis. The optimization process addresses the challenges inherent in real-world traffic scenarios including variations in lighting conditions, diverse vehicle types and dynamic traffic flow patterns. The developed model seeks to enable robust and real-time detection of congestion-related events such as heavy congestion and normal congestion thereby contributing to improved traffic management and overall road safety. Moreover, the paper explores the integration of advanced techniques for congestion pattern analysis including vehicle detection, tracking, and counting. The combination of these features facilitates a comprehensive understanding of traffic dynamics and congestion evolution over time. The proposed approach is trained and evaluated on benchmark datasets and real-world video footage to assess its performance. Experimental results demonstrate the effectiveness of the optimized YOLOv8-based approach in accurately detecting and analyzing road traffic congestion patterns. The system exhibits promising capabilities in handling diverse and challenging scenarios thereby serving as a valuable tool for intelligent transportation systems, urban planning and traffic management. This research significantly contributes to the advancement of video-based traffic analysis, providing a robust solution for monitoring and mitigating congestion-related challenges on road networks.

Automated Audible Truck-Mounted Attenuator Alerts: Vision System Development and Evaluation

Background: The rise in work zone crashes due to distracted and aggressive driving calls for improved safety measures. While Truck-Mounted Attenuators (TMAs) have helped reduce crash severity, the increasing number of crashes involving TMAs shows the need for improved warning systems. Methods: This study proposes an AI-enabled vision system to automatically alert drivers on collision courses with TMAs, addressing the limitations of manual alert systems. The system uses multi-task learning (MTL) to detect and classify vehicles, estimate distance zones (danger, warning, and safe), and perform lane and road segmentation. MTL improves efficiency and accuracy, making it ideal for devices with limited resources. Using a Generalized Efficient Layer Aggregation Network (GELAN) backbone, the system enhances stability and performance. Additionally, an alert module triggers alarms based on speed, acceleration, and time to collision. Results: The model achieves a recall of 90.5%, an mAP of 0.792 for vehicle detection, an mIOU of 0.948 for road segmentation, an accuracy of 81.5% for lane segmentation, and 83.8% accuracy for distance classification. Conclusions: The results show the system accurately detects vehicles, classifies distances, and provides real-time alerts, reducing TMA collision risks and enhancing work zone safety.

An Adaptive Sequencing Approach for Object Detection in Autonomous Vehicles

An Adaptive Sequencing Approach for Object Detection in Autonomous Vehicles

Designing a practical physical framework for AI-enhanced sound detection and localization in vehicles

This paper presents the practical framework of an AI-implemented system currently in development for the detection and localization of external sound events to the vehicle. We delve into the initial block of the automotive project, analyzing sounds of interest and the noise we encounter, addressing the strategic arrangement of the four microphones on the sides of the vehicle, conceived as the sound input system. This configuration not only optimizes signal capture but also acts as sensitive ears, allowing the system to effectively analyze the acoustic environment. To mitigate wind interference, we have designed a microphone placement system that considers the vehicle's aerodynamics. The strategic location and implementation of aerodynamic protectors significantly minimize the impact of wind on the microphones, ensuring a sharper capture of acoustic signals. Complementing this arrangement, we apply advanced noise elimination processes, further enhancing the quality of the captured signals. Although in its early stages, this project aspires to provide effective solutions for vehicular acoustic perception, marking the beginning of a journey towards innovation in the detection and localization of sounds in mobile environments.

Acoustic detection of Unmanned Aerial Vehicles in flight with respect to the signal-to-noise ratio using machine learning

Nowadays, unmanned Aerial vehicles (UAV) are more and more commonly found in the world for a variety of activities. It has become an important task to detect and classify them in order to manage their potential malicious intentions. Current detection and classification methods mostly rely on feature extraction and the use of artificial neural networks that can be heavily impacted by the signal-to-noise ratio (SNR). The approach proposed aims to solve a two-class problem (presence or absence of an UAV) by extracting information on the content of the acoustic source, and then presenting it as input to a neural network. The features used are strongly inspired by the harmonic sound emitted by UAV. Attention is also given on the SNR of the scene. Indeed, various disturbing sources such as trains, helicopters or footsteps are involved from an open library. These signals are added to our recordings of UAVs in flight by tuning their amplitude to obtain different SNRs. The aim is to establish the performance of the detection system as a function of the SNR of the scene.

Underwater acoustic target recognition based on multi-resolution wavelet feature deep learning

The application of underwater acoustic target recognition is extensive, exploration of marine resources, submarine vehicle detection and so on. However, traditional feature extraction methods are susceptible to the influence of complex marine environments and target operating conditions, leading to a significant reduction in the correct recognition rate. This paper introduces an underwater acoustic target recognition method that integrates multi-resolution wavelet features with deep learning algorithms, aiming to overcome the limitations of traditional time-frequency analysis techniques, which are unable to extract multiple signal characteristics simultaneously due to the trade-off between time and frequency resolution. Specifically, the essence of this technique is twofold: (1) Selecting suitable wavelet bases and decomposition levels to capture signal characteristics at different resolutions, effectively seizing the signal's local features within the time-frequency domain; (2) Fusing signal features across different resolutions to optimize the feature extraction process and enhance the distinguishability of target features. Finally, by applying deep learning algorithms to experimentally measured underwater acoustic data, the results demonstrate that this method can effectively enhance the accuracy of underwater acoustic target recognition.

WGS-YOLO: A real-time object detector based on YOLO framework for autonomous driving

The safety and reliability of autonomous driving depends on the precision and efficiency of object detection systems. In this paper, a refined adaptation of the YOLO architecture (WGS-YOLO) is developed to improve the detection of pedestrians and vehicles. Specifically, its information fusion is enhanced by incorporating the Weighted Efficient Layer Aggregation Network (W-ELAN) module, an innovative dynamic weighted feature fusion module using channel shuffling. Meanwhile, the computational demands and parameters of the proposed WGS-YOLO are significantly reduced by employing the Space-to-Depth Convolution (SPD-Conv) and the Grouped Spatial Pyramid Pooling (GSPP) modules that have been strategically designed. The performance of our model is evaluated with the BDD100k and DAIR-V2X-V datasets. In terms of mean Average Precision (mAP0.5), the proposed model outperforms the baseline Yolov7 by 12%. Furthermore, extensive experiments are conducted to verify our analysis and the model’s robustness across diverse scenarios.

TFDNet: A triple focus diffusion network for object detection in urban congestion with accurate multi-scale feature fusion and real-time capability

Vehicle detection in congested urban scenes is essential for traffic control and safety management. However, the dense arrangement and occlusion of multi-scale vehicles in such environments present considerable challenges for detection systems. To tackle these challenges, this paper introduces a novel object detection method, dubbed the triple focus diffusion network (TFDNet). Firstly, the gradient convolution is introduced to construct the C2f-EIRM module, replacing the original C2f module, thereby enhancing the network’s capacity to extract edge information. Secondly, by leveraging the concept of the Asymptotic Feature Pyramid Network on the foundation of the Path Aggregation Network, the triple focus diffusion module structure is proposed to improve the network’s ability to fuse multi-scale features. Finally, the SPPF-ELA module employs an Efficient Local Attention mechanism to integrate multi-scale information, thereby significantly reducing the impact of background noise on detection accuracy. Experiments on the VisDrone 2021 dataset reveal that the average detection accuracy of the TFDNet algorithm reached 38.4%, which represents a 6.5% improvement over the original algorithm; similarly, its mAP50:90 performance has increased by 3.7%. Furthermore, on the UAVDT dataset, the TFDNet achieved a 3.3% enhancement in performance compared to the original algorithm. TFDNet, with a processing speed of 55.4 FPS, satisfies the real-time requirements for vehicle detection.

Vehicle Detection and Counting for Traffic Congestion Estimation Using YOLOv5 and DeepSORT in Smart Traffic Light Application

The escalating number of vehicles on the roads has exacerbated trafficcongestion, presenting a significant and inevitable challenge for roadusers.Trafficcongestionnotonlyincreasesthetraveltimeofroadusers;it also causes air pollution since more vehicles are stuck on theroad. To address this issue, deep learning algorithms, including YOLOv5and DeepSORT, have been leveraged to enable vehicle detection and estimate the number of vehicles through image processing. This study focuses on training a custom YOLOv5 dataset of vehicles and compares its performance with a pretrained YOLOv5 dataset in terms of feasibility for detecting and estimating the number of vehicles. In the proposed approach, YOLOv5 is employed to detect vehicles in video streams, DeepSORT is used for vehicle tracking and counting as they pass through the detection zone, and the number of vehicles in each lane is estimated with the aid of Supervision. The custom dataset's validation demonstrates promising results, with the precision curve indicating convergence for all classes at 97.4% precision and an 87% accuracy in predicting vehicle classes. Additionally, the precision-recall curve assesses the ability to detect individual vehicle categories, such as bus, car, motorcycle, and truck, with accuracies of 68.2%, 95.2%, 79.9%, and 70.1%, respectively. Furthermore, the overall accuracy for detecting all vehicle classes combined is 78.3%. The F1 curve indicates that the system achieves a confidence level of 30.9% F1 score, ensuring 78% confidence in accurately predicting all classes. Both the pretrained and custom datasets exhibit similar accuracy in counting the total number of vehicles passing through the detection zone as well as estimating the number of vehicles in each lane. However, it is evident that the custom dataset's performance can be further improved by incorporating more extensive and diverse datasets during the training process to enhance the accuracy of vehicle detection and estimation. In conclusion, the integration of deep learning algorithms, specificallyYOLOv5 and DeepSORT, offers a promising solution for addressing traffic congestion byefficiently detecting and estimating vehicle numbers, which allow traffic systems to identify which lane is congested and prioritise ensuring the congested lane has a smooth traffic flow. Continued research with larger datasets can lead to further advancements and refined results in the field of traffic management and optimization.

Vehicle detection and tracking algorithm based on improved feature extraction

Vehicle detection and tracking algorithm based on improved feature extraction